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In this paper we analyze the feasibility of a novel neural networks (NN) -based embedded self-test framework for analog devices and systems. The solution that we propose avoids signal quantization, directly dealing with original analog signals, which enables high-accuracy fault detection through lossless signal processing. This is only possible when the self-test unit is also built using analog components...
Continuous time mathematical model of K-winners-take-all (KWTA) neural circuit which can quickly identify the K-winning from N neurons, where 1 les, K <N, whose input signals are larger than of remaining N - K neurons, are proposed. A functional block diagram of corresponding analog neural circuit is presented. For N competitors, such circuit is composed of N feedforward and one feedback hardlimiting...
We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are...
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